Event Description:

Ensembles of climate models are commonly used to improve climate predictions and assess the uncertainties associated with them. Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties—internal and model variabilities. The model uncertainties that contribute greatly to the total uncertainties in most regions may be reduced by weighting the models in the ensemble. Weighting the models according to their performances also holds the promise of further improving ensemble predictions. We used three different Sequential Learning Algorithms (SLAs) to weigh the ensemble members and compared their performances with those of an equally weighted ensemble, a linear regression, and the climatology. Predictions of four different variables—the surface temperature, the zonal and meridional wind, and the pressure—are considered. The spatial distributions of the performances are presented, and the statistical significance of the improvements achieved by the SLAs is tested. The reliability of the SLAs is also tested, and the advantages and limitations of the different performance measures are discussed. It was found that the best performances of the SLAs are achieved when the learning period is comparable to the prediction period. The spatial distribution of the SLAs performance showed that they are skillful and better than the other forecasting methods over large continuous regions. This finding suggests that, despite the fact that each of the ensemble models is not skillful, the models were able to capture some physical processes that resulted in deviations from the climatology and that the SLAs enabled the extraction of this additional information.

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Resume:

Name: Golan Bel

Employment history:

2010-Present Department of Solar Energy and Environmental Physics and Department of Physics, Ben-Gurion University of the Negev